CN111192028A - Block chain-based coal mine yield calculation method, device, equipment and storage medium - Google Patents
Block chain-based coal mine yield calculation method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention relates to the field of mining, in particular to a method, a device, equipment and a storage medium for calculating coal mine yield based on a block chain. A coal mine yield calculation method based on a block chain is applied to any node in the coal mine block chain and comprises the following steps: calculating the yield data of any time period of the coal mine corresponding to the node; and sending the yield data to a coal block chain network. The coal mine can calculate the yield of any time period in real time and send the yield to the block chain network; the block chain network utilizes a consensus mechanism to automatically verify the data, thereby not only improving the authenticity of a monitoring unit for acquiring the coal yield data, but also avoiding the occurrence of the phenomenon that in the prior art, when an enterprise writes data and reports, the data is modified randomly, the data is few, the actual yield is high, and the potential safety hazard is caused. The occurrence of coal mine safety accidents is reduced, and the coal mine safety production efficiency is improved.
Description
Technical Field
The embodiment of the invention relates to the field of mining, in particular to a method, a device, equipment and a storage medium for calculating coal mine yield based on a block chain.
Background
The regulation of coal mine production capacity management method includes: "the coal mine should arrange annual, quarterly and monthly production plans according to the principle of balanced production, and reasonably organize production. Annual raw coal production must not exceed production capacity and monthly raw coal production must not exceed 10% of monthly plans. Without monthly planning, the monthly output must not exceed 1/12 for capacity. Coal mines are required to disclose the production capacity of coal mines and annual and monthly production plans in significant positions, accept social, mass and public opinion supervision, and coal mine enterprises report the production capacity to supervision units intentionally and under-report the production capacity, so that the phenomenon of over-production of the enterprises is concealed, coal mine safety accidents are frequent, and the supervision units are not favorable for mastering the production conditions of the coal mines in real time to supervise the safety production of the coal mine enterprises.
Disclosure of Invention
An object of the embodiments of the present invention is to provide a method, an apparatus, and a device for calculating coal mine yield based on a block chain, so as to solve the above problems.
In order to achieve the above object, the embodiments of the present invention mainly provide the following technical solutions:
in a first aspect, an embodiment of the present invention provides a method for calculating a coal mine yield based on a block chain, which is applied to any node in the coal mine block chain, and includes:
calculating the yield data of any time period of the coal mine corresponding to the node;
and sending the yield data to a coal mine block chain network.
Further, the calculating of the yield data of any time period of the coal mine corresponding to the node comprises:
calculating the output of each working face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any face of the coal mine, the yield is calculated using the following formula:
the yield of the working face is equal to the running distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
Further, the determination of the travel of the shearer comprises:
dividing the working time of the coal mining machine by the time of one stoping of the coal mining machine to obtain the stoping times;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction.
Further, the node is a first node; the method further comprises the following steps: sending a first judgment parameter and the yield data to a coal mine block chain network together so that any one second node except the first node judges whether to achieve consensus with the first node according to the first judgment parameter; the first judgment parameter comprises the coal type density and the mining height of the first node.
Further, the node is a first node; the method further comprises the following steps: receiving coal mining yield data and a second judgment parameter of a certain time period broadcast by any one second node except the first node;
judging whether consensus can be achieved with the second node or not according to the second judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink; the second judgment parameter comprises the coal type density and the mining height of the second node.
Further, determining the travel of the shearer comprises:
determining the starting position and the stopping position of the coal mining machine by using a target detection algorithm in the monitoring video of the working face;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
In a second aspect, the present application further provides a block chain-based coal mine yield calculation method, applied to a monitoring server of a supervision unit, including:
acquiring yield data of a target node from a coal block chain network;
judging whether the acquired yield data of the target node is larger than the pre-stored approved capacity;
if yes, alarming.
In a third aspect, an embodiment of the present invention further provides a device for calculating a coal mine yield based on a block chain, which is applied to any one node in the coal mine block chain, and includes:
the calculation module is used for calculating the yield data of any time period of the coal mine corresponding to the node;
and the sending module is used for sending the yield data to a network.
Further, the calculation module is also used for calculating the yield of each working face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any face of the coal mine, the yield is calculated using the following formula:
the yield of the working face is equal to the running distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
Further, the calculation module is also used for dividing the working time of the coal mining machine by the time of one stoping of the coal mining machine to obtain the stoping times;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction.
Further, the node is a first node; the calculation module is further used for sending the first judgment parameter and the yield data to a coal mine block chain network together so that any one second node except the first node judges whether to achieve consensus with the first node according to the first judgment parameter; the first judgment parameter comprises the coal type density and the mining height of the first node.
Further, the node is a first node; the calculation module is further used for receiving coal mining yield data and second judgment parameters of a certain time period, which are broadcast by any one second node except the first node;
judging whether consensus can be achieved with the second node or not according to the second judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink; the second judgment parameter comprises the coal type density and the mining height of the second node.
Further, the calculation module is also used for determining the starting position and the stopping position of the coal mining machine by using a target detection algorithm in the monitoring video of the working face;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
In a fourth aspect, an embodiment of the present invention further provides a block chain-based coal mine yield calculation apparatus, applied to any one first node in a coal mine block chain, including: at least one processor and at least one memory;
the memory is to store one or more program instructions;
the processor, configured to execute one or more program instructions, is configured to perform the following steps:
calculating the yield data of any time period of the coal mine corresponding to the node;
and sending the yield data to a coal mine block chain network.
Further, the processor is also configured to calculate a production for each face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any face of the coal mine, the yield is calculated using the following formula:
the yield of the working face is equal to the distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
Further, the processor is also used for dividing the working time of the coal mining machine by the time of one stoping of the coal mining machine to obtain the stoping times;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction.
Further, the node is a first node; the processor is further configured to send a first judgment parameter and the yield data to a coal mine block chain network together, so that any one second node except the first node judges whether to achieve consensus with the first node according to the first judgment parameter; the first judgment parameter comprises the coal type density and the mining height of the first node.
Further, the node is a first node; the processor is further used for receiving coal mining yield data and second judgment parameters of a certain time period broadcast by any one second node except the first node;
judging whether consensus can be achieved with the second node or not according to the second judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink; the second judgment parameter comprises the coal type density and the mining height of the second node.
Further, the processor is further configured to determine an initial position and a stop position of the coal mining machine by using a target detection algorithm in the monitoring video of the working face;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which is applied to any one of the first nodes in the coal mine block chain, where the computer-readable storage medium contains one or more program instructions, and the one or more program instructions are configured to be executed to:
calculating the yield data of any time period of the coal mine corresponding to the node;
and sending the yield data to a coal mine block chain network.
Further, the calculating of the yield data of any time period of the coal mine corresponding to the node comprises:
calculating the output of each working face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any face of the coal mine, the yield is calculated using the following formula:
the yield of the working face is equal to the distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
Further, the determination of the travel of the shearer comprises:
dividing the working time of the coal mining machine by the time of one stoping of the coal mining machine to obtain the stoping times;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction.
Further, the node is a first node; the method further comprises the following steps: sending a first judgment parameter and the yield data to a coal mine block chain network together so that any one second node except the first node judges whether to achieve consensus with the first node according to the first judgment parameter; the first judgment parameter comprises the coal type density and the mining height of the first node.
Further, the node is a first node; the method further comprises the following steps: receiving coal mining yield data and a second judgment parameter of a certain time period broadcast by any one second node except the first node;
judging whether consensus can be achieved with the second node or not according to the second judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink; the second judgment parameter comprises the coal type density and the mining height of the second node.
Further, determining the travel of the shearer comprises:
determining the starting position and the stopping position of the coal mining machine by using a target detection algorithm in the monitoring video of the working face;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
The technical scheme provided by the embodiment of the invention at least has the following advantages:
the coal mine yield calculation method based on the block chain provided by the embodiment of the invention is applied to any node in the coal mine block chain, and comprises the following steps: calculating the yield data of any time period of the coal mine corresponding to the node; and sending the yield data to a coal mine block chain network. For the coal mine, the calculation method can calculate the yield data of the coal mine in any time period in real time and report the data to a coal mine block chain network; for a supervision unit, the yield data in the coal mine block chain network can be acquired in real time. And other nodes can use the parameters to judge whether to achieve the consensus with the node, and the yield data is allowed to be reported only if the consensus is achieved, otherwise, the report is not allowed.
According to the technical scheme, the authenticity of the supervision unit for acquiring the coal yield data is improved, the phenomenon that in the prior art, when an enterprise writes data and reports data, the data are modified randomly, the data are few, the actual yield is high, and potential safety hazards are caused is avoided.
Drawings
Fig. 1 is a schematic structural diagram of a coal mine block chain according to an embodiment of the present invention;
FIG. 2 is a flow chart of a block chain-based coal mine production calculation method according to an embodiment of the present invention;
fig. 3 is a schematic view of a coal mining machine provided by an embodiment of the invention, which is located on two sides of a working face;
FIG. 4 is a schematic structural diagram of a block chain-based coal mine production calculation apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a coal mine production calculating device based on a block chain according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
A blockchain is essentially a decentralized database, a string of data blocks that are associated using cryptography.
When the coal mine enterprises report the production capacity to the supervision unit, the production is reported deliberately and under-production is hidden. Which in turn can lead to accidents in coal enterprises.
Based on the characteristics of decentralization, non-falsification, consensus and the like of the block chain, distributed acquisition and management are carried out on the coal mine yield data, storage equipment is dispersed on a plurality of service nodes, and the coal mine yield block chain is built; referring to the schematic structural diagram of the coal mine block chain shown in figure 1; the block chain comprises: data layer, network layer, consensus layer, excitation layer, and contract layer.
The data layer dynamically stores the acquired coal mine yield data in real time, and the data layer comprises data blocks, timestamps, data encryption, chain structures and the like. The data block is output data obtained by coal mine ID, coal type density (different coal types have different densities), mining height of a working face where the coal mining machine is located and real-time calculation. The timestamp adds a 'time' attribute to metadata used for describing coal mine yield data, so that yield corresponds to time, and a supervision unit can conveniently check the yield data at any time. The data encryption comprehensively adopts methods such as a multiple signature technology and a Merkle tree, and the like to carry out encryption verification and content authentication on the stored data, thereby ensuring the accuracy and the integrity of the information.
The network layer implements mechanisms of a distributed network through P2P technology, including a P2P networking mechanism, a data propagation mechanism and a data verification mechanism. In the system, a coal mine is arranged as a node, and communication is kept between the nodes by maintaining a common block chain structure.
The consensus layer mainly comprises a consensus algorithm and a consensus mechanism, and can enable highly dispersed nodes to efficiently achieve consensus on the validity of block data in a decentralized block chain network.
The incentive layer is a 'salary system' for formulating the accounting nodes, and mainly provides certain incentive measures to encourage the nodes to participate in the safety verification work of the block chain. In the system, nodes are encouraged to participate in security verification work and false verification behavior of the constraint nodes.
The contract layer refers to various script codes, algorithm mechanisms, intelligent contracts and the like, and gives the book programmable characteristics.
The application provides a block chain-based coal mine yield calculation method, which is applied to any first node in a coal mine block chain, and refers to a flow chart of the block chain-based coal mine yield calculation method shown in fig. 2; the method comprises the following steps:
step S102, calculating the yield data of any time period of the coal mine corresponding to the node;
wherein, a node represents a single coal mine in a coal mine block chain; in particular to a server in a coal mine;
the time period can be set arbitrarily, and generally, the time period includes one day, one week and one month; or may be a time of once stoping.
And step S104, sending the yield data to a coal mine block chain network.
In order to ensure that the yield data of the uplink is accurate and reliable, when the yield data of one node needs to be uplink, other nodes verify and judge the parameters of the node, and the yield data of the node is allowed to be uplink only when the other nodes and the node achieve common identification.
A consensus mechanism of block chains is utilized; if all the nodes reach consensus, determining that the yield data can be stored in the coal mine block chain network and used for being acquired by an external supervision unit; otherwise the data is not saved.
In order to improve the accuracy of the method for calculating the yield, the following steps are adopted when the yield data of the time period are calculated:
calculating the output of each working face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any one working surface in a node, the yield is calculated by the following formula:
the yield of the working face is equal to the running distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
Wherein, the working surface is a rectangle; adopting the height to be the width of a rectangle; the length of the working surface is the length of a rectangle; the coal type density depends on the type of coal;
the feed distance of the coal mining machine is equal, and the feed distance of each mining of the coal mining machine is equal; the setting can be carried out on a coal mining machine.
The running distance of the coal mining machine is determined by two methods, one method is to convert the recovery times by adopting the working time of the coal mining machine and the time of one recovery of the coal mining machine; calculating the working distance of the coal mining machine according to the length of the working face; another method is to use an image detection algorithm to calculate the travel distance of the coal mining machine;
for the first method, the determination of the travel of the extraction machine comprises the following steps:
dividing the working time of the coal mining machine by the time of one stoping of the coal mining machine to obtain the stoping times;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction, so as to determine the recovery times, the following two methods can be adopted, wherein the first method is that the recovery times are obtained by dividing the working time of the coal mining machine by the time of one recovery of the coal mining machine; the second method is that in the monitoring video of the working face, the times of occurrence of the coal mining machine at the same end point of the working face are determined by using a target detection algorithm; and the position of the end point is the position of the coal mining machine for switching the movement direction.
For the first method, the working time of the coal mining machine is the time length from starting to shutting down;
the mining one-time duration refers to the duration of the coal mining machine from one side of the working face to the other side;
determining the working time of the coal mining machine, and adopting the following steps: detecting the power consumption of the working surface in real time; for one working surface, the data of the electric meter of each working surface can be acquired in real time; when the coal mining machine is started, the power consumption data suddenly rises, and the time point when the power consumption suddenly rises is determined as the starting time point of the coal mining machine; when the coal mining machine stops working, the power consumption data is suddenly reduced, and the time point when the power consumption is suddenly reduced is determined as the shutdown time point of the coal mining machine.
When the time length of one stoping is determined, a mode of presetting a standard value can be adopted; for example, the round-trip time of the coal mining machine on the same working face is generally fixed, so the average time is calculated by statistics and is used as the time length of one-time mining.
In consideration of the fact that the time for one mining may be prolonged when a relatively hard load such as a stone is met or an operation error meets a roof or other emergency situations in an actual scene, the mining time of the coal mining machine is different from one mining time to another, and therefore the time for the coal mining machine to go back and forth is determined by monitoring the acquired image frames and combining a target detection algorithm.
When the target detection algorithm is used for automatically determining the time length of one-time mining of the coal mining machine on a working surface, firstly, the time difference T of two adjacent image frames at the same target position is determined;
referring to the schematic diagram of the coal mining machine shown in fig. 3, the target positions are end positions of the coal mining machine on two sides of the working face; the end point position is the position of the coal mining machine for switching the movement direction; cameras can be arranged at two ends of the working surface; shooting and collecting images in real time; when the image frames of the shearer at the end point are found in the shot images, the moment is determined as a first time point T1; determining the moment when the shearer appears in the image frame of the end point again at a certain time after the shearer switches the movement direction as a second time point T2; subtracting the first time point T1 from the second time point T2 to obtain a time difference T; determining the one-time recovery duration according to the time difference T; wherein, the time length of the extraction once is T/2.
The second method for calculating the number of stoping times is simple and convenient, and assumes that the coal mining machine starts to mine coal, the starting point is the end point on the left side, and the end point is the end point on the right side; a camera is arranged on the right side, and image frames of a right side endpoint are detected in real time; recording the times of reaching the rightmost end of the coal mining machine as the times of recovery; the precondition is that the coal mining machine starts to start from the extreme point at the leftmost side to mine coal; stopping the machine to the extreme point on the rightmost side and stopping coal mining. If the computer is started from the rightmost side, a camera is arranged at the left end, and the number of times of reaching the leftmost end is recorded.
The following describes in detail the calculation of the travel of the extraction machine by means of an image detection algorithm, the method comprising the following steps:
firstly, cameras are required to be arranged at the positions of two end points of a working surface respectively;
shooting and recording the occurrence frequency of the coal mining machine at an end point by a camera; specifically, when the coal mining machine appears at one end point and the running direction is switched, the coal mining machine is determined to appear once, the counter is increased by one, and the reading is 1; if the coal mining machine runs to the other end point and the running direction is switched, the counter is increased by one, and the reading value is changed to 2; and so on;
determining the starting position and the stopping position of the coal mining machine by using a target detection algorithm in the monitoring video of the working face;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
If the coal mining machine is located in the middle of the working face when being started and the running direction is from left to right, the starting end position of the coal mining machine along the running direction of the coal mining machine when being started is an end point on the left side;
if the coal mining machine runs to the rightmost end point, the coal mining machine is captured by the camera, the counter is increased by 1, and the reading is 1;
the coal mining machine changes the running direction and moves from right to left, and if the coal mining machine stops at a middle position; the starting end position of the running direction of the coal mining machine when the coal mining machine stops is the position of the right end point.
The running distance of the coal mining machine is 1 multiplied by the length of the working face (the distance from the left end point position to the starting position of the coal mining machine) + (the distance from the right end point position to the stopping position of the coal mining machine).
In one embodiment, the following steps can be adopted, namely, a plurality of cameras are arranged above the working surface; determining the position of the coal mining machine in the working face by using an image detection algorithm; identifying hydraulic supports between the position of the coal mining machine and the end point position in the image frame and counting the number of the hydraulic supports; the distance from the coal mining machine to the end point can be approximately calculated according to the preset hydraulic support interval. In one embodiment, for any one node, the method further comprises: sending the judging parameters of the node and the yield data to a coal mine block chain network together so that other nodes judge whether to achieve consensus with the node according to the judging parameters; the judgment parameters comprise the coal type density and the mining height of the coal mine corresponding to the node.
If the coal type density is within a preset range and the mining height is within a preset range, determining that the coal yield of the coal mine is accurate; a consensus is reached; if the coal density or the coal mining height is not within a predetermined range, the coal yield is determined to be inaccurate, and consensus cannot be achieved. After consensus is achieved, the coal yield can be stored in the network, and if the consensus is not achieved, the coal yield cannot be stored. Therefore, the supervision unit can acquire a relatively real yield value. This improves the authenticity of coal production. The method avoids the situation that the reported quantity is small but the actual yield is large because a user modifies the yield at will with low cost.
In one embodiment, the node is a first node, and the method further comprises: receiving coal mining yield data and judgment parameters of a certain time period broadcast by any one second node except the first node; judging whether the second node can achieve consensus or not according to the judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink; the parameters include coal density and mining height. In the method, the node can verify the judgment parameters before reporting the yield data to any other node, and if the verification fails, the yield data of other nodes are not allowed to be uploaded to the network.
The application also provides a coal mine yield calculation method based on the block chain, which is applied to a monitoring server of a supervision unit and comprises the following steps:
acquiring coal yield data of a target node from a coal block chain network;
the monitoring server reads the coal yield data of any one or more target nodes from the position where the coal yield data are stored in the coal block chain. The target node is a coal mine enterprise which is interested by a supervision unit or is monitored in a key mode.
Judging whether the obtained coal yield data of the target node is larger than pre-stored approved capacity;
if yes, alarming.
The method aims at the condition that the coal mine enterprises in the block chain exceed the standard for production, the pre-stored approved capacity is the maximum value in the block chain and cannot be exceeded, if the pre-stored approved capacity exceeds the warning line, the coal mine has potential safety hazards, and safety accidents are easy to generate.
An embodiment of the present invention further provides a device for calculating coal mine yield based on a block chain, which is applied to any node in a coal mine block chain, and refer to a schematic structural diagram of a device for calculating coal mine yield based on a block chain shown in fig. 4, where the device includes:
the calculation module 41 is configured to calculate output data of any time period of the coal mine corresponding to the node;
a sending module 42, configured to send the yield data to a network.
Further, the calculation module 41 is further configured to calculate the yield of each working face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any face of the coal mine, the yield is calculated using the following formula:
the yield of the working face is equal to the running distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
Further, the calculating module 41 is further configured to divide the working time of the coal mining machine by the time of one mining of the coal mining machine to obtain the number of times of mining;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction.
Further, the node is a first node; the calculating module 41 is further configured to send a first judgment parameter and the yield data to a coal mine block chain network together, so that any one of the second nodes except the first node judges whether to achieve consensus with the first node according to the first judgment parameter; the first judgment parameter comprises the coal type density and the mining height of the first node.
Further, the node is a first node; the calculating module 41 is further configured to receive coal mining yield data and a second judgment parameter of a certain time period, which are broadcast by any one second node except the first node;
judging whether consensus can be achieved with the second node or not according to the second judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink; the second judgment parameter comprises the coal type density and the mining height of the second node.
Further, the calculation module 41 is further configured to determine, in the monitoring video of the working surface, an initial position and a stop position of the coal mining machine by using a target detection algorithm;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
The embodiment of the invention also provides a coal mine yield calculation device based on the block chain, which is applied to any node in the coal mine block chain, and the structural schematic diagram of the coal mine yield calculation device based on the block chain shown in the attached figure 5 is shown; the apparatus comprises: at least one processor 51 and at least one memory 52;
the memory 52 is used to store one or more program instructions;
the processor 51 is configured to execute one or more program instructions to perform the following steps:
calculating the yield data of any time period of the coal mine corresponding to the node;
and sending the yield data to a coal mine block chain network.
Further, the processor 51 is further configured to calculate the production of each working face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any face of the coal mine, the yield is calculated using the following formula:
the yield of the working face is equal to the distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
Further, the processor 51 is further configured to divide the working time of the coal mining machine by the time of one mining of the coal mining machine to obtain the number of times of mining;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction. Further, the node is a first node; the processor 51 is further configured to send a first judgment parameter to the coal mine block chain network together with the yield data, so that any one of the second nodes except the first node judges whether to achieve consensus with the first node according to the first judgment parameter; the first judgment parameter comprises the coal type density and the mining height of the first node.
Further, the node is a first node; the processor 51 is further configured to receive coal mining yield data and a second judgment parameter of a certain time period, which are broadcast by any one second node except the first node;
judging whether consensus can be achieved with the second node or not according to the second judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink; the second judgment parameter comprises the coal type density and the mining height of the second node.
Further, the processor 51 is further configured to determine, in the monitoring video of the working face, a start position and a stop position of the coal mining machine by using a target detection algorithm;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
An embodiment of the present invention further provides a computer-readable storage medium, which is applied to any node in a coal mine block chain, where the computer-readable storage medium contains one or more program instructions, and the one or more program instructions are configured to be executed to:
calculating the yield data of any time period of the coal mine corresponding to the node;
and sending the yield data to a coal mine block chain network.
Further, the calculating of the yield data of any time period of the coal mine corresponding to the node comprises:
calculating the output of each working face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any face of the coal mine, the yield is calculated using the following formula:
the yield of the working face is equal to the distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
Further, the determining of the number of stopes comprises:
dividing the working time of the coal mining machine by the time of one stoping of the coal mining machine to obtain the stoping times;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction.
Further, the node is a first node; the method further comprises the following steps: sending a first judgment parameter and the yield data to a coal mine block chain network together so that any one second node except the first node judges whether to achieve consensus with the first node according to the first judgment parameter; the first judgment parameter comprises the coal type density and the mining height of the first node.
Further, the node is a first node; the method further comprises the following steps: receiving coal mining yield data and a second judgment parameter of a certain time period broadcast by any one second node except the first node;
judging whether consensus can be achieved with the second node or not according to the second judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink; the second judgment parameter comprises the coal type density and the mining height of the second node.
Further, determining the travel of the shearer comprises:
determining the starting position and the stopping position of the coal mining machine by using a target detection algorithm in the monitoring video of the working face;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. A coal mine yield calculation method based on a block chain is characterized by being applied to any node in the coal mine block chain and comprising the following steps:
calculating the yield data of any time period of the coal mine corresponding to the node;
and sending the yield data to a coal mine block chain network.
2. The method of claim 1,
calculating the yield data of any time period of the coal mine corresponding to the node, wherein the calculation comprises the following steps:
calculating the output of each working face in the coal mine;
calculating the total output of the coal mine according to the output of each working face;
for any face of the coal mine, the yield is calculated using the following formula:
the yield of the working face is equal to the running distance of the coal mining machine, the feed distance of the coal mining machine, the mining height and the coal density.
3. The method of claim 2, wherein the determining of the operational path of the shearer comprises:
dividing the working time of the coal mining machine by the time of one stoping of the coal mining machine to obtain the stoping times;
calculating the running distance of the coal mining machine according to the recovery times and the length of the working face;
determining the working time of the coal mining machine comprises:
detecting the power consumption of the working surface in real time;
determining the time point of sudden rise of the power consumption as the starting time point of the coal mining machine;
determining the time point of sudden drop of the power consumption as the shutdown time point of the coal mining machine;
determining the once-stoping duration comprises:
in the monitoring video of the working face, determining the time length of once mining of the coal mining machine on the working face by using a target detection algorithm, wherein the method comprises the following steps:
determining the time difference of two adjacent image frames at the same end point of the working surface of the coal mining machine;
determining the time length of the extraction once according to the time difference;
the position of the end point is the position of the coal mining machine for switching the movement direction.
4. The method of claim 1, wherein the node is a first node; the method further comprises the following steps: sending a first judgment parameter of a first node and the yield data to a coal mine block chain network together so that any one second node except the first node judges whether to achieve consensus with the first node according to the first judgment parameter; the first judgment parameter comprises the coal type density and the mining height of the first node.
5. The method of claim 1, wherein the node is a first node; the method further comprises the following steps: receiving coal mining yield data and second judgment parameters of any second node except the first node in a certain time period and broadcasted by the second node;
judging whether consensus can be achieved with the second node or not according to the second judgment parameter; if consensus is achieved, the coal mining yield data of the second node allows uplink;
the second judgment parameter comprises the coal type density and the mining height of the second node.
6. The method of claim 2, wherein determining the operational path of the shearer comprises:
determining the starting position and the stopping position of the coal mining machine by using a target detection algorithm in the monitoring video of the working face;
the running distance of the coal mining machine is equal to the accumulated times of the coal mining machine appearing at the two ends, namely the length of the working face, which is identified by the cameras arranged at the two ends of the working face, the distance from the starting end position along the running direction of the coal mining machine to the starting position of the coal mining machine when the coal mining machine is started, and the distance from the starting end position along the running direction of the coal mining machine to the stopping position of the coal mining machine when the coal mining machine is stopped.
7. A coal mine yield calculation method based on a block chain is characterized in that a monitoring server applied to a supervision unit comprises the following steps:
acquiring coal yield data of a target node from a coal block chain network;
judging whether the obtained coal yield data of the target node is larger than pre-stored approved capacity;
if yes, alarming.
8. A block chain-based coal mine yield calculation device is applied to any node in a coal mine block chain, and comprises:
the calculation module is used for calculating the yield data of any time period of the coal mine corresponding to the node;
and the sending module is used for sending the yield data to a coal mine block chain network.
9. A blockchain-based coal mine production computing apparatus, comprising: at least one processor and at least one memory;
the memory is to store one or more program instructions;
the processor, configured to execute one or more program instructions to perform the method of any of claims 1-7.
10. A computer-readable storage medium having one or more program instructions embodied therein for being executed to perform the method of any one of claims 1-7.
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Application publication date: 20200522 |